Machine learning-based event analysis for customized contract generation and negotiation in enhanced security peer-to-peer interaction applications

ABSTRACT

A system for machine learning-derived contract generation is provided. The system comprises: a machine learning engine and a controller configured to: input historical and streaming interaction data into the machine learning engine, wherein the machine learning engine is trained by the historical and streaming interaction data; determine one or more machine learning-derived interaction patterns for a resource transfer between the first user device and the second user device, wherein the one or more machine learning-derived interaction patterns comprise calculated exposure levels for one or more events for completing the resource transfer; based on the machine learning-derived interaction patterns, generate the resource transfer contract for transferring a resource from the first user device to the second user device, wherein the resource transfer contract comprises a sequential flow of the one or more events; and distribute the resource transfer contract to the first user device and the second user device.

BACKGROUND

Resource transfers, such as peer-to-peer resource transfers, have grownin popularity in recent years. One of the greatest challenges inpeer-to-peer systems is the fact that the users are advised to executetransfers with only those that they know and trust. Even thoughpeer-to-peer systems enable seamless resource transfer capabilities in amobile space, the security aspect has been the main obstacle for notbeing further implemented in larger-scale and/or sensitive interactions.Due to the ease, speed, and finality of the interactions involved,peer-to-peer resource transfers may be subject to misappropriationattempts. For example, there are a number of misappropriation caseswhere a user causes another user to believe that they are transferringvalid resources while they also propose the resource transfer to manyother users. As such, there exists a need for an improved resourcetransfer system for generating customizable resource transfers withcontract-executed, event-based tracking for improved resource transfersecurity and confirmation.

BRIEF SUMMARY

The following presents a simplified summary of one or more embodimentsof the invention in order to provide a basic understanding of suchembodiments. This summary is not an extensive overview of allcontemplated embodiments and is intended to neither identify key orcritical elements of all embodiments, nor delineate the scope of any orall embodiments. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later.

A system for machine learning-derived contract generation is provided.The system comprises: a machine learning engine; and a controllerconfigured for generating a resource transfer contract between a firstuser device and a second user device, the controller comprising a memorydevice with computer-readable program code stored thereon, acommunication device connected to a network, and a processing device,wherein the processing device is configured to execute thecomputer-readable program code to: input historical and streaminginteraction data into the machine learning engine, wherein the machinelearning engine is trained by the historical and streaming interactiondata; determine one or more machine learning-derived interactionpatterns for a resource transfer between the first user device and thesecond user device, wherein the one or more machine learning-derivedinteraction patterns comprise calculated exposure levels for one or moreevents for completing the resource transfer; based on the machinelearning-derived interaction patterns, generate the resource transfercontract for transferring a resource from the first user device to thesecond user device, wherein the resource transfer contract comprises asequential flow of the one or more events; and distribute the resourcetransfer contract to the first user device and the second user device.

In one embodiment, the processing device is further configured toexecute the computer-readable program code to: calculate an exposurelevel for each of the one or more events of the resource transfercontract based on the interaction patterns; identify that the exposurelevel for at least one of the one or more events exceeds a predeterminedthreshold; and based on the exposure level exceeding the predeterminedthreshold, modify the sequential flow of the one or more events, whereinmodifying the sequential flow of the one or more events comprisesinserting an additional security event to the sequential flow.

In another embodiment, the processing device is further configured toexecute the computer-readable program code to: receive a modification tothe sequential flow of the one or more events from at least one of thefirst user device and the second user device; generate a modifiedresource transfer contract based on the modification; and calculate amodified exposure level for the modified resource transfer contract,wherein the one or more events and the modification are input into themachine learning engine.

In yet another embodiment, the processing device is further configuredto execute the computer-readable program code to iteratively modify thesequential flow of the one or more events until the modified exposurescore is below a predetermined threshold limit. In yet anotherembodiment, the processing device is further configured to execute thecomputer-readable program code to terminate the resource transfercontract when the modified exposure level exceeds a predeterminedthreshold limit.

In yet another embodiment, the historical and streaming interaction datacomprises one or more of the group consisting of resource data, aresource transfer type, user data of the first user and the second user,historical resource transfer data, and historical misappropriation data.In yet another embodiment, the historical misappropriation data furthercomprises previously identified misappropriation interactions andunauthorized user logs.

In yet another embodiment, the machine learning engine further comprisesa natural language processing module configured to determine the one ormore machine learning-derived interaction patterns for the resourcetransfer based on the historical and streaming interaction data.

In yet another embodiment, the resource transfer contract is anexecutable tracking module, and wherein the processing device isconfigured to execute the computer-readable program code to install theexecutable module on the first user device and the second user device,and wherein the executable tracking module is configured to trackcompletion of the one or more events

A system for natural language processing-enhanced resource transfercontract security is provided. The system comprises: a machine learningengine comprising a natural language processing module; and a controllerconfigured for securely customizing a resource transfer contract betweena first user device and a second user device, the controller comprisinga memory device with computer-readable program code stored thereon, acommunication device connected to a network, and a processing device,wherein the processing device is configured to execute thecomputer-readable program code to: input misappropriation interactiondata into the natural language processing module, wherein the naturallanguage processing module is trained by the misappropriationinteraction data; extract one or more keyword identifiers from themisappropriation interaction data, wherein the one or more keywordidentifiers are associated with misappropriation interactions; based onthe one or more identifiers, calculate an exposure level for theresource transfer contract; compare the exposure level of the resourcetransfer contract to a predetermined exposure threshold level; and basedon the exposure level of the resource transfer contract not exceedingthe predetermined exposure threshold level, distribute the resourcetransfer contract to the first user device and the second user device.

In one embodiment, the resource transfer contract comprises a sequentialflow of one or more events, and wherein the processing device is furtherconfigured to execute the computer-readable program code to: calculatean individual exposure level for each of the one or more events of theresource transfer contract based on the interaction patterns; identifythat the individual exposure level for at least one of the one or moreevents exceeds the predetermined exposure threshold level; and based onthe individual exposure level exceeding the predetermined exposurethreshold level, modify the sequential flow of the one or more events,wherein modifying the sequential flow of the one or more eventscomprises inserting an additional security event to the sequential flow.

In another embodiment, the processing device is further configured toexecute the computer-readable program code to iteratively modify thesequential flow of the one or more events until the exposure level forthe resource transfer contract is below the predetermined exposurethreshold level.

In yet another embodiment, the keyword identifier comprises at least oneof vocabulary, syntax, semantics, grammar, and word frequency patterns.In yet another embodiment, the misappropriation interaction datacomprises historical and streaming data.

A computer-implemented method for or machine learning-derived contractgeneration is also provided. The computer-implemented method comprises:inputting historical and streaming interaction data into the machinelearning engine, wherein the machine learning engine is trained by thehistorical and streaming interaction data; determining one or moremachine learning-derived interaction patterns for a resource transferbetween a first user device and a second user device, wherein the one ormore machine learning-derived interaction patterns comprise calculatedexposure levels for one or more events for completing the resourcetransfer; based on the machine learning-derived interaction patterns,generating the resource transfer contract for transferring a resourcefrom the first user device to the second user device, wherein theresource transfer contract comprises a sequential flow of the one ormore events; and distributing the resource transfer contract to thefirst user device and the second user device.

In one embodiment, the method further comprises: calculating an exposurelevel for each of the one or more events of the resource transfercontract based on the interaction patterns; identifying that theexposure level for at least one of the one or more events exceeds apredetermined threshold; and based on the exposure level exceeding thepredetermined threshold, modifying the sequential flow of the one ormore events, wherein modifying the sequential flow of the one or moreevents comprises inserting an additional security event to thesequential flow.

In another embodiment, the method further comprises: receiving amodification to the sequential flow of the one or more events from atleast one of the first user device and the second user device;generating a modified resource transfer contract based on themodification; and calculating a modified exposure level for the modifiedresource transfer contract, wherein the one or more events and themodification are input into the machine learning engine.

In yet another embodiment, the method further comprises iterativelymodifying the sequential flow of the one or more events until themodified exposure score is below a predetermined threshold limit. In yetanother embodiment, the method further comprises terminating theresource transfer contract when the modified exposure level exceeds apredetermined threshold limit.

In yet another embodiment, the historical and streaming interaction datacomprises one or more of the group consisting of resource data, aresource transfer type, user data of the first user and the second user,historical resource transfer data, and historical misappropriation data.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined with yet other embodiments, further details of whichcan be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described embodiments of the invention in general terms,reference will now be made to the accompanying drawings, wherein:

FIG. 1 provides a contract-based resource transfer system environment,in accordance with one embodiment of the invention;

FIG. 2 provides a block diagram of a user device, in accordance with oneembodiment of the invention;

FIG. 3 provides a block diagram of an contract-based resource system, inaccordance with one embodiment of the invention;

FIG. 4 provides a block diagram of an entity system, in accordance withone embodiment of the invention;

FIG. 5 provides a high level process flow for contract-based resourcetransfer generation, in accordance with one embodiment of the invention;

FIG. 6 provides a process diagram of an exemplary contract execution, inaccordance with one embodiment of the invention;

FIG. 7 provides a machine learning contract template generator systemenvironment, in accordance with one embodiment of the invention;

FIG. 8 provides a high level process flow for machine learning trainingand contract template generation, in accordance with one embodiment ofthe invention;

FIG. 9 provides a high level process flow for contract templatecustomization and finalization, in accordance with one embodiment of theinvention;

FIG. 10 provides a high level process flow for peer-to-peer resourcetransfer execution, in accordance with one embodiment of the invention;

FIG. 11 provides a high level process flow for natural languageprocessing exposure event extraction, in accordance with one embodimentof the invention; and

FIG. 12 provides a high level process flow for exposure-based contractmodification, in accordance with one embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the invention, as described herein, leverage complex,specific-use computer system to provide a novel approach for generatingand executing customizable contract-based resource transfers forimproved resource transfer security and delivery confirmation. Thesystem of the present invention is configured to generate resourcetransfers between users and/or devices (e.g., a peer-to-peerinteraction) comprising conditions of the transfer as well as one ormore events that may trigger processes, steps, or actions contained inthe contract, such as a final transfer of the resource between parties.

The system enables a contract generation process wherein participantscustomize and agree on details and conditions of a resource transferfrom predetermined and/or learned or recommended options. Conditions ofa resource transfer may include, for example a delivery of goods,transfer conditions, transfer terms, a transfer amount, a date andlocation on which the resource transfer or one or more steps or eventsof the resource transfers are required to be completed, and the like.The conditions and triggering events established in the resourcetransfer contract generated by the system are mutually agreed upon byall parties involved in the transfer before the transfer is initiated.Defined conditions and triggering events for resource transfers may bedefined or modified based on, for example, the type or amount of aresource to be transferred, the users and/or user devices participatingin the transfer, or the like. Embodiments of the invention may furtherinclude an exposure scoring component, wherein individual events may bedynamically tailored for a customized resource transfer based on thesecurity requirements and potential for exposure.

In some embodiments, the system may generate a sequential flow of eventsthat must be completed in order to successfully trigger a transfer of aresource and complete the resource transfer. These chains of events maybe used to track the status of complex resource transfers and identifypoints of failure in event flows. The system is further configured tocancel a resource transfer after determining a failure to successfullyexecute a required event or meet a condition, wherein resources arereleased back to an original resource location (e.g., an originatinguser and/or user device). In each of the stages of the agreed uponresource transfer protocol, each user may be required to add supportingdata or documents, such as shipment receipts, delivery notifications, orthe like. Similarly, independent third parties may be involved to ensureand verify that the resource or any other goods or services have beendelivered (e.g., tickets have been scanned, a movie or concert entrancehappened (i.e., tickets were used), etc.).

Furthermore, the system provides a conflict resolution moduleincorporating third party verification and authentication in the eventof a disputed event. The conflict resolution module may includeadditional delivery conditions or triggering events as well as contractcancelation and resource return conditions. In the event of conflicts, aprocess is agreed upon to resolve the conflict which may require thirdparty involvement (e.g., third party verification).

The described system can be provided as a custom service for high-endpeer-to-peer or large-scale entity (i.e., entity-to-entity)interactions. This process requires both parties to start with aninitial contract template based on a resource type and customize theprocess with agreed upon steps and additional parties. Following anagreement on the final contract, the contract is translated into anexecutable file that is installed on the devices of the involved users(e.g., in a mobile device application in which the protocol isembedded). The invention is configured to codify a resource transfercontract into an executable file format that is installed on theparticipating user devices and that runs as a distributed peer-to-peerprotocol agreed upon by all parties involved at the beginning of theinteraction. The executable file specifies what exact steps will need totake place, at what exact dates/times, what parties need to approve theprocess steps completion, what conditions are required for a completeresource transfer, what conditions are required for resource return orcontract termination, or the like.

As the steps are completed, the resource transfers, claims, and otherautomatic steps are completed in the background. The backend of theproposed solution includes collecting signals or notifications from allthe authorized parties and devices at the specified steps of theprocess. For instance, if a delivery company is required to report thecompletion of the delivery of a resource, there is an API (applicationprogramming interface) configured to receive the information on theback-end system via communication with one or more other systems (e.g.,third party systems).

In contrast to the present invention, current peer-to-peer resourcetransfer methods lack reliable tools for tracking and confirmingdelivery of resources for both involved parties leading to the increasedpotential for exposure and misappropriation. Instead, the presentsolution provides significant advantages over current peer-to-peerinteractions or resource transfers. Due to the pre-determined agreement,both parties can agree on all of the details of the process beforeexecution of the resource transfer. The underlying distributed protocolprovides a level of confidence against potential exposure ormisappropriation as the peer-to-peer resource transfer becomes moresecure and conditional on various terms that protect both parties.Additionally, both parties have the option to customize the protocolbased on the specific type of resource transfer, a potential exposurelevel of the interaction, an amount of a resource to be transferred, andthe like. For example, a resource transfer for a first smaller resourceamount for a basketball contest ticket may be treated very differentlythan a resource transfer for a second, larger resource amount forelectronics. Even further, both of these examples may be different froman entity (e.g., a company) resource transfer for a third, even largerresource amount. In these examples within the invention, eachinteraction may have different event flows, different parties involved,different security requirements, and the like.

In some embodiments of the invention, the invention further leveragesartificial intelligence and machine learning technology to determine andgenerate templates for contract negotiation and execution between users.A machine learning engine may be configured to receive data input suchas a resource transfer type, a resource amount, a potential exposure ormisappropriation level, an exposure appetite for the users involved, andthe like to generate an appropriate template.

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Like numbers refer to elements throughout. Wherepossible, any terms expressed in the singular form herein are meant toalso include the plural form and vice versa, unless explicitly statedotherwise. Also, as used herein, the term “a” and/or “an” shall mean“one or more,” even though the phrase “one or more” is also used herein.Furthermore, when it is said herein that something is “based on”something else, it may be based on one or more other things as well. Inother words, unless expressly indicated otherwise, as used herein “basedon” means “based at least in part on” or “based at least partially on.”

As used herein, the term “user” may refer to any entity or individualassociated with the contract-based resource transfer system describedherein. In some embodiments, a user may be a computing device user, aphone user, a mobile device application user, a customer of an entity orbusiness, a system operator, and/or employee of an entity (e.g., afinancial institution). In one embodiment, a user may be a customeraccessing a user account via an associated user device, wherein datafrom an interaction between the user and another user and/or entity ismonitored, analyzed, and/or processed by the system. In a specificembodiment, a user is a requestor of an interaction or transaction withanother user or entity, wherein the user is attempting to transfer orexchange resources with another user or entity. In some embodiments,identities of an individual may further include online handles,usernames, identification numbers (e.g., Internet protocol (IP)addresses), aliases, family names, maiden names, nicknames, or the like.In some embodiments, the user may be an individual or an organization(i.e., a charity, business, company, governing body, or the like).

As used herein the term “user device” may refer to any device thatemploys a processor and memory and can perform computing functions, suchas a personal computer or a mobile device, wherein a mobile device isany mobile communication device, such as a cellular telecommunicationsdevice (i.e., a cell phone or mobile phone), a mobile Internet accessingdevice, or other mobile device. Other types of mobile devices mayinclude laptop computers, tablet computers, wearable devices, cameras,video recorders, audio/video player, radio, global positioning system(GPS) devices, portable digital assistants (PDAs), automated tellermachines (ATMs), or any combination of the aforementioned. The devicemay be used by the user to access the system directly or through anapplication, online portal, internet browser, virtual private network,or other connection channel.

As used herein, the term “entity” may be used to include anyorganization or collection of users that may interact with thecontract-based resource transfer system. An entity may refer to abusiness, company, or other organization that either maintains oroperates the system or requests use and accesses the system. In someembodiments, an entity may refer to a financial entity. The terms“financial institution” and “financial entity” may be used to includeany organization that processes financial transactions including, butnot limited to, banks, credit unions, savings and loan associations,investment companies, stock brokerages, resource management firms,insurance companies and the like. In specific embodiments of theinvention, use of the term “bank” is limited to a financial entity inwhich account-bearing customers conduct financial transactions, such asaccount deposits, withdrawals, transfers and the like. In otherembodiments, an entity may be a business, organization, a governmentorganization or the like that is not a financial institution.

As used herein, “authentication information” may refer to anyinformation that can be used to authenticate an identify a user and/or auser device. For example, a system may prompt a user to enterauthentication information such as a username, a password, a personalidentification number (PIN), a passcode, biometric information (e.g.,voice authentication, a fingerprint, and/or a retina scan), an answer toa security question, a unique intrinsic user activity, such as making apredefined motion with a user device. This authentication informationmay be used to at least partially authenticate the identity of the user(e.g., determine that the authentication information is associated witha device and/or account) and determine that the user has authority toaccess an account or system or otherwise execute an interaction. In someembodiments, the system may be owned or operated by an entity. In suchembodiments, the entity may employ additional computer systems, such asauthentication servers, to validate and certify resources inputted bythe plurality of users within the system.

To “monitor” is to watch, observe, or check something for a specialpurpose over a period of time. The “monitoring” may occur periodicallyover the period of time, or the monitoring may occur continuously overthe period of time. In some embodiments, a system may actively monitor adata source, data stream, database, or data archive, wherein the systemmay be configured to reach out to the data source and watch, observe, orcheck the data source for changes, updates, variations, patterns, andthe like. In other embodiments, a system may passively monitor a datasource or data stream, wherein the data source or data stream providesinformation to the system and the system then watches, observes, orchecks the provided information. In some embodiments, “monitoring” mayfurther comprise analyzing or performing a process on something such asa data source or data stream either passively or in response to anaction or change in the data source or data stream.

As used herein, an “interaction” may refer to any action orcommunication between one or more users, one or more entities orinstitutions, and/or one or more devices or systems within the systemenvironment described herein. For example, an interaction may refer to auser interaction with a system or device, wherein the user interactswith the system or device in a particular way. In one embodiment,interactions may be received or extracted from a data stream (e.g., inreal-time). An interaction may include user interactions with a userinterface of a user application (e.g., clicking, swiping, text or dataentry, etc.), authentication actions (e.g., signing-in, username andpassword entry, PIN entry, etc.), account actions or events (e.g.,account access, fund transfers, document or record views, etc.) and thelike. In another example, an interaction may refer to a usercommunication via one or more channels (i.e., phone, email, text,instant messaging, brick-and-mortar interaction, and the like) with anentity and/or entity system to complete an operation or perform anaction with an account associated with user and/or the entity. In aspecific embodiment, an interaction may comprise a transfer or exchangeof resources (e.g., funds, data (i.e., files), goods, service, or thelike) between users and/or devices either directly or via anintermediate system (e.g., an entity system and/or the contract-basedresource transfer system described below). In a specific embodiment, aninteraction may comprise a peer-to-peer transfer or exchange ofresources at least partially executed over a network. In someembodiments, an interaction may additionally comprise an exchange ofresource and/or physical goods or services.

FIG. 1 provides a contract-based resource transfer system environment100, in accordance with one embodiment of the invention. As illustratedin FIG. 1 , contract-based resource transfer system 130 is operativelycoupled, via a network 101, to the user device(s) 110 (e.g., a pluralityof user devices 110 a-110 d) and the entity system(s) 120. In this way,the contract-based resource transfer system 130 can send information toand receive information from the user device 110 and the entity system120. In the illustrated embodiment, the plurality of user devices 110a-110 d provide a plurality of communication channels through which theentity system 120 and/or the contract-based resource transfer system 130may communicate with the user 102 over the network 101.

FIG. 1 illustrates only one example of an embodiment of the systemenvironment 100. It will be appreciated that in other embodiments, oneor more of the systems, devices, or servers may be combined into asingle system, device, or server, or be made up of multiple systems,devices, or servers. It should be understood that the servers, systems,and devices described herein illustrate one embodiment of the invention.It is further understood that one or more of the servers, systems, anddevices can be combined in other embodiments and still function in thesame or similar way as the embodiments described herein.

The network 101 may be a system specific distributive network receivingand distributing specific network feeds and identifying specific networkassociated triggers. The network 101 may also be a global area network(GAN), such as the Internet, a wide area network (WAN), a local areanetwork (LAN), or any other type of network or combination of networks.The network 101 may provide for wireline, wireless, or a combinationwireline and wireless communication between devices on the network 101.The network 101 may further comprise a peer-to-peer communicationnetwork.

In some embodiments, the user 102 is an individual interacting with oneor more entity systems 120 and/or other user devices via a user device110 while a data stream or flow between the user device 110 and theentity system 120 and/or other user devices is intercepted and monitoredby the contract-based resource transfer system 130 over the network 101.In some embodiments a user 102 is a user requesting service from theentity or interacting with an account maintained by the entity system120. In an alternative embodiment, the user 102 is an individualinteracting with the contract-based resource transfer system 130 overthe network 101 and monitoring input of information from the entitysystems 120 to and from the contract-based resource transfer system 130for processing and analysis (e.g., an employee of the entity operatingand/or monitoring the systems 120, 130). In another specific embodiment,the user 102 in an individual interacting with another user to completean interaction or transaction between the two users (e.g., apeer-to-peer interaction). For example, the interaction may be executedbetween user devices 110 of the two users directly. In an alternativeexample, the interaction may be processed through another system such asentity system 120 and/or contract-based resource transfer system 130.

FIG. 2 provides a block diagram of a user device 110, in accordance withone embodiment of the invention. The user device 110 may generallyinclude a processing device or processor 202 communicably coupled todevices such as, a memory device 234, user output devices 218 (e.g., auser display device 220, or a speaker 222), user input devices 214(e.g., a microphone, keypad, touchpad, touch screen, and the like), acommunication device or network interface device 224, a power source244, a clock or other timer 246, a visual capture device such as acamera 216, a positioning system device 242, such as a geo-positioningsystem device or GPS device, an accelerometer, and the like. In oneembodiment, the camera 216 may include a scanner, barcode reader, or anyother image capturing device or sensor configured to capture an image orscan readable indicia (e.g., a barcode, label, or the like). Theprocessing device 202 may further include a central processing unit 204,input/output (I/O) port controllers 206, a graphics controller orgraphics processing device (GPU) 208, a serial bus controller 210 and amemory and local bus controller 212.

The processing device 202 may include functionality to operate one ormore software programs or applications, which may be stored in thememory device 234. For example, the processing device 202 may be capableof operating applications such as the user application 238. The userapplication 238 may then allow the user device 110 to transmit andreceive data and instructions from the other devices and systems of theenvironment 100. The user device 110 comprises computer-readableinstructions 236 and data storage 240 stored in the memory device 234,which in one embodiment includes the computer-readable instructions 236of a user application 238. In some embodiments, the user application 238allows a user 102 to access and/or interact with other systems such asthe entity system 120. In one embodiment, the user application 238 maybe configured to allow a user 102 to request, initiate, and/or receivean interaction with another device or system. In some embodiments, theuser application 238 is a resource transfer application, wherein theuser application 238 is configured to allow a user to transfer, receive,or exchange, a resource with other user devices (e.g. via peer-to-peerinteractions). In some embodiments, the memory device 234 may storeinformation or data generated by the contract-based resource transfersystem 130 and/or by the processes described herein. In a specificembodiment, the memory device 234, and more specifically the datastorage 240, may be configured to store a resource used in interactionsdescribed herein.

The processing device 202 may be configured to use the communicationdevice 224 to communicate with one or more other devices on a network101 such as, but not limited to the entity system 120 and thecontract-based resource transfer system 130. In this regard, thecommunication device 224 may include an antenna 226 operatively coupledto a transmitter 228 and a receiver 230 (together a “transceiver”),modem 232. The processing device 202 may be configured to providesignals to and receive signals from the transmitter 228 and receiver230, respectively. The signals may include signaling information inaccordance with the air interface standard of the applicable BLEstandard, cellular system of the wireless telephone network and thelike, that may be part of the network 201. In this regard, the userdevice 110 may be configured to operate with one or more air interfacestandards, communication protocols, modulation types, and access types.By way of illustration, the user device 110 may be configured to operatein accordance with any of a number of first, second, third, and/orfourth-generation communication protocols and/or the like. For example,the user device 110 may be configured to operate in accordance withsecond-generation (2G) wireless communication protocols IS-136 (timedivision multiple access (TDMA)), GSM (global system for mobilecommunication), and/or IS-95 (code division multiple access (CDMA)), orwith third-generation (3G) wireless communication protocols, such asUniversal Mobile Telecommunications System (UMTS), CDMA2000, widebandCDMA (WCDMA) and/or time division-synchronous CDMA (TD-SCDMA), withfourth-generation (4G) wireless communication protocols, and/or thelike. The user device 110 may also be configured to operate inaccordance with non-cellular communication mechanisms, such as via awireless local area network (WLAN) or other communication/data networks.The user device 110 may also be configured to operate in accordanceBluetooth® low energy, audio frequency, ultrasound frequency, or othercommunication/data networks.

The user device 110 may also include a memory buffer, cache memory ortemporary memory device operatively coupled to the processing device202. Typically, the one or more applications 238, are loaded into thetemporary memory during use. As used herein, memory may include anycomputer readable medium configured to store data, code, or otherinformation (e.g., an executable resource transfer contract). The memorydevice 234 may include volatile memory, such as volatile Random AccessMemory (RAM) including a cache area for the temporary storage of data.The memory device 234 may also include non-volatile memory, which can beembedded and/or may be removable. The non-volatile memory mayadditionally or alternatively include an electrically erasableprogrammable read-only memory (EEPROM), flash memory or the like.

FIG. 3 provides a block diagram of a contract-based resource transfersystem 130, in accordance with one embodiment of the invention. Thecontract-based resource transfer system 130 generally comprises acontroller 301, a communication device 302, a processing device 304, anda memory device 306.

As used herein, the term “controller” generally refers to a hardwaredevice and/or software program that controls and manages the varioussystems described herein such as the user device 110, the entity system120, and/or the contract-based resource transfer system 130, in order tointerface, monitor, and manage data flow between systems while executingcommands to control the systems. In some embodiments, the controller 301may be integrated into or be placed in one or more of the systemsdescribed herein. In other embodiments, the controller 301 may be aseparate system or device. In some embodiments, the controller 301 mayperform one or more of the processes, actions, or commands describedherein.

As used herein, the term “processing device” or “processor” generallyincludes circuitry used for implementing the communication and/or logicfunctions of the particular system. For example, a processing device mayinclude a digital signal processor device, a microprocessor device, andvarious analog-to-digital converters, digital-to-analog converters, andother support circuits and/or combinations of the foregoing. Control andsignal processing functions of the system are allocated between theseprocessing devices according to their respective capabilities. Theprocessing device may include functionality to operate one or moresoftware programs based on computer-readable instructions thereof, whichmay be stored in a memory device.

The processing device 304 is operatively coupled to the communicationdevice 302 and the memory device 306. The processing device 304 uses thecommunication device 302 to communicate with the network 101 and otherdevices on the network 101, such as, but not limited to the user device110 and the entity system 120. As such, the communication device 302generally comprises a modem, server, or other device for communicatingwith other devices on the network 101.

As further illustrated in FIG. 3 , the contract-based resource transfersystem 130 comprises computer-readable instructions 310 stored in thememory device 306, which in one embodiment includes thecomputer-readable instructions 310 of a resource tracking application312, a resource transfer contract generator 313, and a machine learningengine 315. The resource transfer contract generator 313 may beconfigured to generate customized resource transfer contracts betweenone or more user devices based on details of the transfer such as theresource associated with the transfer (e.g., type of resource, amount ofthe resource, etc.), the parties or users involved in the transfers, anda calculated exposure score associated with the transfer. The resourcetransfer generator 313 may be further configured to generate one or moreconditions and/or trigger events to be executed by one or more of theusers associated with the resource transfer in order to successfullytrigger a transfer of the resource or another process in defined by acontract protocol. In some embodiments, the resource transfer contractgenerator 313 may further include an exposure scoring module forcalculating exposure scores associated with resource transfers andevents. The resource tracking application 312 may be configured to trackcompletion or execution of the one or more conditional events defined ina generated resource transfer to provide a record of a resource transferprogression and eventual successful delivery or failed execution by oneor more of the involved parties. In some embodiments, the resourcetracking application 312 may be configured to communicate with one ormore third party systems or devices 140, as illustrated in FIG. 1 , fortracking and confirming event execution. In one embodiment, the resourcetracking application 312 may be configured to trigger a release of aheld resource to one or more of the users involved in the resourcetransfer. The machine learning engine 315 may further comprise a naturallanguage processing module.

In some embodiments, the memory device 306 includes data storage 308 forstoring data related to the system environment, but not limited to datacreated and/or used by the resource tracking application 312, theresource transfer generator 313, and the machine learning engine 315.Data stored in the data storage 308 may comprise a user informationdatabase 314, and a resource storage 316, misappropriation database 318,machine learning models 320, and natural language processing module 322.

The user information database 314 is used to store information and dataassociated with one or more users and/or user devices as describedherein. In some embodiments, the user information database 314 mayinclude user identifying information, user account information, userinteraction information (e.g., historical interactions, account actionsor events, transactions, communications, inputs), user deviceinformation (e.g., device identification information, device serialnumbers, digital signatures, device security tokens), exposure ormisappropriation information, and the like. Resource storage 316 mayinclude permanent or temporary storage for one or more resourcesassociated with a resource transfer described herein, wherein theresource may be held separate from the users associated with theresource transfer.

In one embodiment of the invention, the contract-based resource transfersystem 130 may associate with applications having computer-executableprogram code that instruct the processing device 304 to perform certainfunctions described herein. In one embodiment, the computer-executableprogram code of an application associated with the user device 110and/or the entity systems 120 may also instruct the processing device304 to perform certain logic, data processing, and data storingfunctions of the application.

Embodiments of the contract-based resource transfer system 130 mayinclude multiple systems, servers, computers or the like maintained byone or many entities. In some embodiments, the contract-based resourcetransfer system 130 may be part of the entity systems 120. In otherembodiments, the entity systems 120 are distinct from the contract-basedresource transfer system 130. The contract-based resource transfersystem 130 may communicate with the entity systems 120 via a secureconnection generated for secure encrypted communications between the twosystems either over the network 101 or alternative to the network 101.

As illustrated in detail in FIG. 4 , the environment 100 furtherincludes one or more entity systems 120 which are connected to the userdevice 110 and the contract-based resource transfer system 130. Theentity systems 120 may be associated with one or more entities,institutions or the like. The entity systems 120 generally comprise acommunication device 402, a processing device 404, and a memory device406 further comprising data storage 408. The entity systems 120 comprisecomputer-readable instructions 410 stored in the memory device 406,which in one embodiment includes the computer-readable instructions ofan entity application 412. The entity systems 120 may communicate withthe user device 110 and the contract-based resource transfer system 130to provide access to accounts and resources stored and maintained on theentity systems 120. In some embodiments, the entity system 120 maycommunicate with the contract-based resource transfer system 130 duringa requested interaction or resource transfer between one or more usersand/or user devices in real-time, wherein user interactions or resourcetransfers may be monitored and tracked by the contract-based resourcetransfer system 130. In some embodiments, data storage 408 comprisesuser information database 416 and/or resource storage 420 to eithersupplement or replace similar data storages or databases on thecontract-based resource transfer system 130 as previously discussed.

The systems of the environment 100 may be used to generate and executecustomizable resource transfer contracts with event-based tracking forresource transfer security and delivery confirmation. As previouslydiscussed, the system of the present invention is configured to generateresource transfers between users and/or devices (e.g., a peer-to-peerinteraction) and define one or more conditions and events for triggeringa transfer of resources, wherein the transfer is dependent uponsuccessful execution of the one or more conditional events. Theconditions and events established in the resource transfer contractgenerated by the system are mutually agreed upon by all parties involvedin the transfer before the transfer is initiated. It should beunderstood that in some embodiments, the systems and methods describedherein may operate as part of a peer-to-peer interaction performedbetween users and/or user devices (e.g., user mobile devices), whereinthe system and methods modify traditional peer-to-peer interactions toinclude enhanced resource tracking and improved interaction security.

As used herein, a “resource transfer” may refer to any defined agreementbetween users for transferring or exchanging a resource along with anyrequired conditions for permitting the transfer that have been mutuallyagreed upon by the users. A generated resource transfer may include allconditions and specifications of a requested transfer of resourcebetween users. In some embodiments, a resource transfer may be anexecutable file generated by the system for performing a transfer orexchange of resource between users, wherein the devices of the users arecontrolled by the system to execute the transfer according to the agreedupon terms should the required conditions be successfully executed. Thesystem is configured to receive the requested requirements form theusers and generate the resource transfer for completing the requestedinteraction. Conditions or requirements associated with a resourcetransfer may include a type of resource (e.g., funds, data, goods,services) to be transferred or exchanged, an amount of a resource to betransferred or exchanged, resource locations or destinations forresource delivery and origination (e.g., an account or the like),physical delivery locations for resources (e.g., user devices, networklocations, GPS-determined locations), and the like.

In some embodiments, conditions defined in the resource transfer mayfurther comprise one or more events, actions, or the like to be executedby one or more of the users to complete the resource transfer. Thesystem defines one or more conditional events in the generated resourcetransfer contract, wherein triggering a transfer of resources isdependent on execution of at least one conditional event. Typically, theone or more conditional events include at least one triggering event fortriggering the transfer of the resource. As the conditions defined inthe resource transfer must be mutually agreed upon by all users involvedin the transfer, the users must also agree on any conditional events andtriggering events for triggering the transfer of resources.

In some embodiments, a generated resource transfer may comprise a chainof conditional event that includes a series of events dependent on oneanother, wherein a failure to execute one of the events along the chainmay cause the system to cancel the associated resource transfer. Forexample, a chain of conditional events may comprise events A, B, C, D,and E, wherein the events are required to be executed in order, andwherein event E is a triggering event for triggering a transfer of aresource. In this example, if events A and B are successfully executed,but event C is failed, the system may be configured to cancel theresource transfer before the chain reaches triggering event E. In otherembodiments, the system may be configured to generate custom resourcetransfers and chains of conditional events, wherein more than one eventin a chain must be failed before a resource transfer is canceled. Insome embodiments, the system may be configured to lock a chain ofconditional events after the conditions and terms of the resourcetransfer have been agreed upon or accepted by the users (i.e., viaassociated user devices) associated with the resource transfer. In thisway, the system preserves the integrity of the mutually agreed uponconditions and prevents unauthorized modifications to the resourcetransfer after the process of the transfer has been initiated.

Non-limiting examples of conditional events and/or triggering events mayinclude, receiving a message from one or more users or user devices(e.g., a delivery confirmation message), scanning of readable indicia(e.g., a barcode of a ticket, a tracking label of a package, etc.),capturing of an image (e.g. of a delivered package or good), determininga location of a device or resource (e.g., via GPS), or the like. Inother examples, a conditional event and/or triggering event may betime-based, wherein an event is considered having been executed at apredetermined time or after a predetermined period of time has elapsed(e.g., 24 hours after the transfer was initiated, at the beginning of aplay, movie, sporting event, etc.)

FIG. 5 provides a high level process flow for contract-based resourcetransfer generation, in accordance with one embodiment of the invention.As illustrated in block 510, the system is configured to first provide aresource transfer contract template to the first user and the seconduser. In the illustrated embodiment, the resource transfer contract is afor a customer peer-to-peer resource transfer. In response to receivingthe initial contract template, the first user and the second user submitrevisions or modifications to the contract template. Requested revisionsor modifications submitted from each user are transmitted to each userdevice so that each user may review and agree to conditions before thecontract template is modified. In one embodiment, the contract is anexecutable distributed protocol. The revisions or modifications may betransmitted to a server, wherein any revisions or modifications arepushed to the user devices to maintain up-to-date versions of thecontract.

As illustrated in block 520, the system is configured to generate afinal resource transfer contract comprising the modifications to theconditions and/or triggering events by the users. The system isconfigured to receive approval (e.g., user sign-off) for the finalresource transfer contract from the first user and the second user. Asillustrated in block 530, following approval of the resource transfercontract, the system is configured to codify the resource transfercontract into an executable file (i.e., distributed protocol), whereinthe agreed upon conditions and triggering events are embedded in theexecutable file. The system is configured to transmit the executablefile to the first user device and the second user device, wherein theexecutable file is installed on the user devices (e.g., in a mobileapplication). In some embodiments, the executable file may be installedon the user devices before finalization of the contract, whereinnegotiation of a contract template my occur on the user devices.

FIG. 6 provides process diagram for an exemplary contract execution, inaccordance with one embodiment of the invention. As illustrated in theexemplary executable peer-to-peer contract, a contract may comprise oneor more conditions such as one or more resource amounts, resource types,resource transfer conditions, delivery conditions for resources, a timeand/or location for a resource delivery, conditions for validating usersand/or one or more steps of the resource transfer, security conditions(e.g., authentication), return conditions for resources, and the like.The one or more conditions are agreed upon by all involved parties andfinalized. Upon the conditions and/or triggering events being completedor met, the system is configured to mark the conditions and/ortriggering events as completed within the contract. In some embodiments,entries in a resource transfer contract comprise a step or event portionand a corresponding action portion, wherein the action portion of theentry is triggered upon the corresponding step or event portion beingperformed.

FIG. 7 provides a machine learning contract template generator systemenvironment, in accordance with one embodiment of the invention. Thesystem leverages artificial intelligence systems, and in particular, amachine learning engine comprising one or more models 702 for processingthe data input into the system to generate contract templates. Asillustrated in the embodiment of FIG. 7 , the machine learning model 702receives input from a variety of data sources including resourcetransfer information data 704 (e.g., resource amount, type format), usermisappropriation exposure scoring data 706, resource transfer databases708 (i.e., historical interaction data), and misappropriation claim andalert data repositories 710. The machine learning engine furthercomprises a natural language processing module comprising a model 714.The natural language processing model receives historicalmisappropriation log files 712. The machine learning model 702 alsoreceives recent misappropriation tactics or strategies.

As previously discussed, the system further comprises an exposurescoring component. As illustrated in FIG. 7 , the machine learning modeloutputs one or more recommended conditions and/or triggering events forscoring by the exposures scoring component 718. The exposure scoringcomponent receives exposure threshold values 720 of the users as well asany other involved parties (e.g., third parties). As illustrated inblock 722, the system analyzes exposure scores and generates an initialcontract template such that a calculated exposure score for the initialcontract is lower than a predetermined exposure threshold value. Thecontract template is output and sent to the users for negotiation andrevision as discussed herein.

FIG. 8 provides a high level process flow for machine learning trainingand contract template generation, in accordance with one embodiment ofthe invention. As illustrated in block 810, data from historicalresource transfers (e.g., resource amount, types, users involved, etc.)between users across all channels is input into the system. In someembodiments, the input data may further include streaming data frominteractions in real-time. As previously discussed, a variety of datasources are input into the machine learning model. In block 820, thesystem further inputs misappropriation rates of all individual parties,a misappropriation rate for resource transfer type, a geographiclocation, and other factors. At block 830, the system further inputs aresource transfer type (e.g., specific resources, resource types andformats). As illustrated in block 840, the system further inputs labeleddata from historical and recent misappropriation cases and tactics(e.g., from claims, alert and analyst processes, report logs etc.).

As previously discussed, in some embodiments, the system furthercomprises a natural language processing module. As illustrated in block850, log files for misappropriation entries are analyzed through thenatural language processing module to determine breaking points orpotential failure events within a resource transfer (e.g., wrongresource delivery, no resource delivery, resource contracted to morethan one other user, defective or misrepresented resource).

The machine learning model is trained using the resource transfer data,event data, user and misappropriation records, labeled data, and logfiles with both recent and historical entries in block 860. In block870, the machine learning model provides prediction for the overallresource transfer in terms of misappropriation potential (i.e., exposurelevel scoring), and outputs key stages that require protection orconfirmation for misappropriation potential (e.g., deliveryconfirmation). As illustrated in block 880, resulting outputs areanalyzed and clustered based on main characteristics, such as exposurelevels, misappropriation levels, resource transfer type, resourcetransfer amount, and the like. In block 890, the system generates acontract template combining the machine learning output for the resourcetransfer data (i.e., type, amount, users, exposure levels, etc.) withany required steps to secure the process.

FIG. 9 provides a high level process flow for contract templatecustomization and finalization, in accordance with one embodiment of theinvention. As discussed above, the system is configured to enable allinvolved parties to review, negotiate, and modify a contract templateprior to contract finalization. The system first collects data forinitial processing. In block 910, the system inputs resource transferdata (e.g., amount, type, resources involved, users, exposure levels,and the like) to the machine learning system. Additionally, each user'smisappropriation record is looked up (e.g., previous misappropriationclaims, etc.). In block 920, each user's individual exposure andsecurity requirements (i.e., exposure thresholds) as well as contractcomplexity requirements are inputted. Next, in block 930, the machinelearning model analyzes the received input entries and outputs arecommended contract template based on historical and streaming data.

As illustrated in block 940, the system provides the users (i.e., viacorresponding user devices) with the contract template generated by themachine learning model to ensure the security and misappropriationpotential of the overall process. In block 950, each user requestsrevisions or modifications to the template. Other users are notified ofthe requested changes via an application on the user devices.

In some embodiments, the contract includes hard and soft requestpositions from the users, wherein a hard position is non-negotiable, anda soft position may be negotiated or further modified. In someembodiments, a dispute regarding a hard position may trigger automatictermination of a contract. In block 960, both the hard and soft requestspositions from each user are received and matched. In some embodiments,matching items from all users are automatically accepted, whilenon-matching items are negotiated by the users through the applicationand the resource transfer contract.

At block 970, if all the users are in agreement, the system continues toblock 980, wherein a resulting final contract is codified into anexecutable file with each step agreed upon as marked as a step in theprocess with specific action items and any additional parties ofinterest (e.g., users, approvers, validators, third parties). Finally,in block 990, all users are asked to approve or sign off on theelectronic peer-to-peer contract. A final version of the contract islocked and maintained by the system as a reference document to preserveimmutability of the contract.

FIG. 10 provides a high level process flow for peer-to-peer resourcetransfer execution, in accordance with one embodiment of the invention.As illustrated in block 1010, in the event of custom peer-to-peercontract execution, each user agrees on the final contract, and thefinal codified contract is distributed to all involved users. At block1020, at each step, checks are made to determine that each stage iscompleted or incomplete (e.g., barcode of the delivery shipment isscanned, or the like). Each completed step generates a notification forall the users involved. At block 1030, in some embodiments, users may beasked to provide supporting data for steps.

As illustrated in block 1040, when a predetermined set of steps iscompleted, the resources to be transferred are moved to a temporarystorage location before the final execution of the transfer. The systemdetermines whether the one or more conditional events have been executedby at least one of the users and/or user devices associated with agenerated resource transfer. In order to determine whether conditionalevents have been executed, the system may communicate with one or moreadditional systems (i.e., on the back-end) to track the events. Forexample, the system may be configured to communicate with a packagedelivery tracking system to determine tracking and delivery status of apackage. In other embodiments, the system may utilize an internal clockor timer for determining execution of time-based conditional events. Inyet other embodiments, the system may rely on communication from one ormore devices associated with a resource transfer for determiningexecution of one or more events, wherein the one or more devicestransmit updates or event execution confirmation to the system. In oneembodiment, the system may only consider an event successfully executedif all users involved in the resource transfer confirm or agree that theevent was successfully executed. In some embodiments, the system mayrely on communication with a third party intermediary system or devicefor impartial confirmation of event execution and/or resource delivery.

In order to ensure delivery of a resource and/or any other goods orservices and prevent potential misappropriation, the system may beconfigured to hold a resource to be transferred or exchanged during theresource transfer until the conditions of the resource transfer arefulfilled or terminated. In one embodiment, wherein the resourcetransfer is associated with a transfer of funds, the system may beconfigured to receive the temporarily hold the funds until one or moreconditional events are successfully executed or until the resourcetransfer is otherwise terminated. For example, a resource transferbetween users may comprise a transfer of funds in exchange for goods.The system may be configured to impartially hold the funds on behalf ofthe users until any conditional events are successfully executed, or theresource transfer is otherwise terminated. In this way, the systemprovides an intermediary resource storage location to provide additionalsecurity to the resource transfer and prevent potential misappropriationattempts. In some embodiments, the system may further comprise a thirdparty system or device configured to temporarily hold the resourcesassociated with the resource transfer on behalf of the users, whereinthe system is configured to trigger release of the resources from thethird party in response to execution of the conditional events. In someembodiments, the third party system of device may be associated with anentity maintaining the system described herein. In other embodiments,the third party may be a separate entity contracted to hold and releasethe resource in response to execution of the conditions of a resourcetransfer generated by the system.

Alternatively, if the one or more conditions or triggering events havenot been successfully executed or otherwise failed, the system maycancel the resource transfer. Upon cancellation, the system may beconfigured to automatically return any held resources back to a point oforigination, such as an original user, user device, or resource locationassociated with the resource. At block 1050, in the event that steps arenot agreed upon, conflict resolution aspects of the contract areactivated, wherein third party input or other forms of input are sought.

As illustrated in block 1060, when all steps in the peer-to-peercontract are completed, the resource transfer is agreed as beingcompleted and the resource is released. Based on determining successfulcompletion of the one or more conditional events defined by the resourcetransfer, the system triggers the transfer of the resource based on theone or more conditional events having been successfully executedaccording to the conditions or criteria defined and mutually agreed uponin the resource transfer. In one embodiment, triggering transfer of theresource may comprise releasing the resource from a temporary hold bythe system and/or a third party system or device to a recipient (e.g., auser resource location or user device) defined by the conditions andterms of the resource transfer. For example, in this way, a resource maybe transferred from a first user device to a second user device.

In block 1070, any shadow processes such as resource releases ortransfers, claim submissions, misappropriation alerts, or the like areautomatically processed by the back-end of the system. At block 1080, inthe event of identified misappropriation, associated users are marked inthe systems with the reported claims. Corresponding misappropriationexposure scores for any following resource transfers are updatedaccording to the learned exposure from recent and previous transfers.

FIG. 11 provides a high level process flow for natural languageprocessing exposure event extraction, in accordance with one embodimentof the invention. Initially, at block 110, the system inputsmisappropriation logs. In some embodiments, the misappropriation logsmay comprise written records and/or spoken or voice records. At block1120, for each transaction type the system is configured to process thelog files with basic natural language processing steps such astokenization, syntax, and semantic analysis. In this way, the system isconfigured to extract keywords associated with misappropriation andexposure tactics and strategies through machine learning and statisticalcorrelation of expressions and the flow of keywords in the records asillustrated in block 1130.

In some embodiments, natural language processing data collected by thesystem may further include, but is not limited to, identified languages,dialects, grammar, syntax or sentence structure (i.e., construction,length, complexity, etc.), vocabulary, misspellings, abbreviations, wordfrequency patterns, slang, colloquialisms, pronunciations, vocalintonations, timbre, pitch, cadence and other similar languagequalities. Natural language data may further include cognitive markersor identifiers (i.e., patterns in cognition and information delivery andreceipt) and other signature markers such as mistakes or failures toperform a task (e.g., provide a password or piece of information forauthentication). Natural language data may be collected across multiplecommunication channels including voice (e.g., via voice recognitiontechnology), video, brick-and-mortar, and text-based channels whileleveraging machine learning to generate a complete natural languagerecord. For example, the system may collect natural language data duringa user interaction with a customer support call center via a phone line,wherein the natural language system or module may be configured toextract vocal or articulated identifiers characteristic of the user inorder to generate the natural language record. The natural languagerecord data may be used by the system to identify potentialmisappropriation in historical and streaming interaction data.

At block 1140, the system then calculates the misappropriation exposureassociated with each event E or sequence of events E_(i-j). The system,at block 1150, further is configured to lookup historical resourcetransfer records (e.g., for both valid and invalid resource transfers)for remediation steps for potential misappropriation or exposure (e.g.,third party involvement in delivery, claims rate reduction by x %). Atblock 1160, the system is configured to input the exposure thresholdfrom the users involved (e.g., users and parties involved) and calculatethe target exposure threshold score. At block 1170, the systemcalculates a total exposure including remediation steps associated withthe exposable events in the potential resource transfer event flow. Theevent flow is iterated by the system such that the total exposure isreduced below the determined and set threshold. At block 1180, thesystem outputs the resulting contract template with potential resourcetransfer events for the calculated misappropriation exposure record.

In some embodiments, the system is configured to generate one or moreconditional events, such as a chain of conditional events, for trackingthe resource transfer based on the exposure score. As previouslydiscussed, the chain of conditional events includes at least onetriggering event for triggering the transfer of the resource uponexecution. Based on the calculated exposure score, the system may beconfigured to generate the conditional events and/or triggering events.For example, in response to a high exposure score, the system may beconfigured to generate additional events for triggering the transfer ortracking the progress of the transfer. In one embodiment, the system maybe configured to place a triggering event in the chain of conditionalevents based on the exposure score. For example, for a resource transferassociated with a high exposure score, the system may place a triggeringevent further downstream in the chain of events to increase transfersecurity. In another embodiment, the system may be configured to add oneor more additional or interconnected triggering events required fortriggering transfer of the resource in response to a high exposurescore. In yet another embodiment, one or more triggering events may berequired to be executed simultaneously or within a predetermined timeperiod to trigger the transfer. Alternatively, in response to lowerexposure score (e.g., for a resource transfer for a small amount or witha familiar user), the system may remove one or more conditional eventsor otherwise simplify the resource to reduce an amount of computingresources used to process the transfer due to a high confidence in thetransfer security.

In one embodiment, the system may be configured to calculate exposurescores for individual conditional events, such as events that arechained together. For example, a first event may be associated withhigher potential for exposure than a second event. In this way, thesystem may identify one or more individual event that has a higherchance for potential misappropriation and increase security or requireadditional confirmation for the one or more individual events.

In another embodiment, the system is configured to recalculate theexposure score in real-time based on execution of one or more of theconditional events executed before a final triggering event thatreleases the resource. In some embodiments, the system may recalculatean exposure score based on how a conditional event is executed. Forexample, a shipping event for a package may be executed and confirmedthrough a shipping entity scanning and receiving the package. The systemmay determine through the received scanning information that the packagewas shipped via a method that does not require a signature on delivery,does not include delivery insurance, does not include a tracking number,or the like. In response, the system may recalculate the exposure scorefor the resource transfer, wherein the exposure score may be modified(i.e., increased or decreased) based on the additional information.

In another embodiment, the system may recalculate an exposure scorebased on determined failure to execute one or more of the conditionalevents defined in the resource transfer. For example, the system mayincrease an exposure score based on a failed conditional event. Forexample, a failed conditional event may include a late or postponedshipment or delivery, a failure to provide a tracking number, a declinedresource location (i.e., an account), or the like. In other embodiments,a failed conditional event, such as a triggering event, may insteadautomatically cancel the resource transfer.

FIG. 12 provides a high level process flow for exposure-based contractmodification, in accordance with one embodiment of the invention. Asillustrated in block 1210 the system calculates an overall exposurethreshold for the resource transfer by incorporating the user exposurethresholds for all involved users (e.g., through a summation of theindividual exposure levels). An exposure score, as calculated by thesystem, is a composite factor that may be input into the system tomodify or enhance generated resource transfers. In some embodiments,calculation of the exposure score may be based on, for example, theresource itself (e.g. the type of resource, an amount of the resource,etc.) and/or the one or more users and associated devices participatingin the resource transfer. For example, transfer of a larger amount of aresource or a transfer to an unknown user or stranger may factor into ahigher calculated exposure score for a resource transfer.

At block 1220, the system provides this collected information to themachine learning module configured to generate contract templates for aresource transfer along with any additional required information (e.g.,user information). At block 1230, a template is outputted and sharedwith the users. Each template has hard blocks that cannot be customizedand soft blocks that can be customized with some limitations oncustomizations in some cases. At block 1240, the system receivesfeedback from users in terms of accepted steps and customizationrequests (i.e., revisions or modifications).

As illustrated in block 1250, the system analyzes the customization ormodification requests and consolidates the requests to determine if theusers have agreed upon a solution in block 1260. In one embodiment, atblock 1265, a solution is identified by the system and the resultingcontract template is output to the user devices. Alternatively, if asolution is not identified, the system continues to block 1270 where theagreed requests and requests with potential compromise possibility areinput back into the machine learning model to check resulting thresholdchecks. If the exposure threshold is exceeded, the contract negotiationand generation process may be terminated at blocks 1275 and 1280.

As will be appreciated by one of ordinary skill in the art, the presentinvention may be embodied as an apparatus (including, for example, asystem, a machine, a device, a computer program product, and/or thelike), as a method (including, for example, a business process, acomputer-implemented process, and/or the like), or as any combination ofthe foregoing. Accordingly, embodiments of the present invention maytake the form of an entirely software embodiment (including firmware,resident software, micro-code, and the like), an entirely hardwareembodiment, or an embodiment combining software and hardware aspectsthat may generally be referred to herein as a “system.” Furthermore,embodiments of the present invention may take the form of a computerprogram product that includes a computer-readable storage medium havingcomputer-executable program code portions stored therein. As usedherein, a processor may be “configured to” perform a certain function ina variety of ways, including, for example, by having one or morespecial-purpose circuits perform the functions by executing one or morecomputer-executable program code portions embodied in acomputer-readable medium, and/or having one or more application-specificcircuits perform the function. As such, once the software and/orhardware of the claimed invention is implemented the computer device andapplication-specific circuits associated therewith are deemedspecialized computer devices capable of for generating and executingcustomizable resource transfers with contract-executed, event-basedtracking for resource transfer security and delivery confirmation.

It will be understood that any suitable computer-readable medium may beutilized. The computer-readable medium may include, but is not limitedto, a non-transitory computer-readable medium, such as a tangibleelectronic, magnetic, optical, infrared, electromagnetic, and/orsemiconductor system, apparatus, and/or device. For example, in someembodiments, the non-transitory computer-readable medium includes atangible medium such as a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a compact discread-only memory (CD-ROM), and/or some other tangible optical and/ormagnetic storage device. In other embodiments of the present invention,however, the computer-readable medium may be transitory, such as apropagation signal including computer-executable program code portionsembodied therein.

It will also be understood that one or more computer-executable programcode portions for carrying out the specialized operations of the presentinvention may be required on the specialized computer includeobject-oriented, scripted, and/or unscripted programming languages, suchas, for example, Java, Perl, Smalltalk, C++, SAS, SQL, Python, ObjectiveC, and/or the like. In some embodiments, the one or morecomputer-executable program code portions for carrying out operations ofembodiments of the present invention are written in conventionalprocedural programming languages, such as the “C” programming languagesand/or similar programming languages. The computer program code mayalternatively or additionally be written in one or more multi-paradigmprogramming languages, such as, for example, F #.

It will further be understood that some embodiments of the presentinvention are described herein with reference to flowchart illustrationsand/or block diagrams of systems, methods, and/or computer programproducts. It will be understood that each block included in theflowchart illustrations and/or block diagrams, and combinations ofblocks included in the flowchart illustrations and/or block diagrams,may be implemented by one or more computer-executable program codeportions. These one or more computer-executable program code portionsmay be provided to a processor of a special purpose computer forgenerating and executing customizable resource transfers withcontract-executed, event-based tracking for resource transfer securityand delivery confirmation, and/or some other programmable dataprocessing apparatus in order to produce a particular machine, such thatthe one or more computer-executable program code portions, which executevia the processor of the computer and/or other programmable dataprocessing apparatus, create mechanisms for implementing the stepsand/or functions represented by the flowchart(s) and/or block diagramblock(s).

It will also be understood that the one or more computer-executableprogram code portions may be stored in a transitory or non-transitorycomputer-readable medium (e.g., a memory, and the like) that can directa computer and/or other programmable data processing apparatus tofunction in a particular manner, such that the computer-executableprogram code portions stored in the computer-readable medium produce anarticle of manufacture, including instruction mechanisms which implementthe steps and/or functions specified in the flowchart(s) and/or blockdiagram block(s).

The one or more computer-executable program code portions may also beloaded onto a computer and/or other programmable data processingapparatus to cause a series of operational steps to be performed on thecomputer and/or other programmable apparatus. In some embodiments, thisproduces a computer-implemented process such that the one or morecomputer-executable program code portions which execute on the computerand/or other programmable apparatus provide operational steps toimplement the steps specified in the flowchart(s) and/or the functionsspecified in the block diagram block(s). Alternatively,computer-implemented steps may be combined with operator and/orhuman-implemented steps in order to carry out an embodiment of thepresent invention.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of, and not restrictive on, the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations and modifications ofthe just described embodiments can be configured without departing fromthe scope and spirit of the invention. Therefore, it is to be understoodthat, within the scope of the appended claims, the invention may bepracticed other than as specifically described herein.

What is claimed is:
 1. A system for machine learning-derived contractgeneration, the system comprising: a machine learning engine; and acontroller configured for generating a resource transfer contractbetween a first user device and a second user device, wherein theresource transfer contract is for a peer-to-peer resource transfer, thecontroller comprising a memory device with computer-readable programcode stored thereon, a communication device connected to a network, anda processing device, wherein the processing device is configured toexecute the computer-readable program code to: input historical andstreaming interaction data into the machine learning engine, wherein themachine learning engine is trained by the historical and streaminginteraction data, wherein the historical and streaming interaction datacomprises data associated with peer-to-peer transfers of resources atleast partially executed over a peer-to-peer network; determine one ormore machine learning-derived interaction patterns for the resourcetransfer between the first user device and the second user device,wherein the one or more machine learning-derived interaction patternscomprise calculated exposure levels for one or more events forcompleting the resource transfer; based on the one or more machinelearning-derived interaction patterns, generate the resource transfercontract for transferring a resource from the first user device to thesecond user device, wherein the resource transfer contract comprises asequential flow of the one or more events; transmit the resourcetransfer contract to the first user device and the second user device;receive revisions to the resource transfer contract from at least one ofa first user associated with the first user device and a second userassociated with the second user device; transmit agreed-upon conditionsto the first user device and the second user device; receive approval ofthe resource transfer contract from the first user via the first userdevice and the second user via the second user device; generate, basedon receiving the approval, a final resource transfer contract comprisingthe agreed-upon conditions; codify the final resource transfer contractcomprising the agreed-upon conditions into an executable file forinstallation on the first user device and the second user device,wherein the executable file runs on the first user device and the seconduser device as a distributed peer-to-peer protocol; and distribute theexecutable file to the first user device and the second user device. 2.The system of claim 1, wherein the processing device is furtherconfigured to execute the computer-readable program code to: calculatean exposure level for each of the one or more events of the resourcetransfer contract based on the one or more machine learning-derivedinteraction patterns; identify that the exposure level for at least oneof the one or more events exceeds a predetermined threshold; and basedon the exposure level exceeding the predetermined threshold, modify thesequential flow of the one or more events, wherein modifying thesequential flow of the one or more events comprises inserting anadditional security event to the sequential flow.
 3. The system of claim1, wherein the processing device is further configured to execute thecomputer-readable program code to: receive a modification to thesequential flow of the one or more events from at least one of the firstuser device and the second user device; generate a modified resourcetransfer contract based on the modification; and calculate a modifiedexposure level for the modified resource transfer contract, wherein theone or more events and the modification are input into the machinelearning engine.
 4. The system of claim 3, wherein the processing deviceis further configured to execute the computer-readable program code toiteratively modify the sequential flow of the one or more events untilthe modified exposure level is below a predetermined threshold limit. 5.The system of claim 3, wherein the processing device is furtherconfigured to execute the computer-readable program code to terminatethe resource transfer contract when the modified exposure level exceedsa predetermined threshold limit.
 6. The system of claim 1, wherein thehistorical and streaming interaction data comprises one or more ofresource data, a resource transfer type, user data of a first userassociated with the first user device and a second user associated withthe second user device, historical resource transfer data, andhistorical misappropriation data.
 7. The system of claim 6, wherein thehistorical misappropriation data further comprises previously identifiedmisappropriation interactions and unauthorized user logs.
 8. The systemof claim 1, wherein the machine learning engine further comprises anatural language processing module configured to determine the one ormore machine learning-derived interaction patterns for the resourcetransfer contract based on the historical and streaming interactiondata.
 9. The system of claim 1, wherein the processing device isconfigured to execute the computer-readable program code to, whendistributing the executable file, install the executable file on thefirst user device and the second user device, and wherein the executablefile is configured to track completion of the one or more events.
 10. Acomputer-implemented method for machine learning-derived contractgeneration, the computer-implemented method comprising: inputtinghistorical and streaming interaction data into a machine learningengine, wherein the machine learning engine is trained by the historicaland streaming interaction data, wherein the historical and streaminginteraction data comprises data associated with peer-to-peer transfersof resources at least partially executed over a peer-to-peer network;determining one or more machine learning-derived interaction patternsfor a resource transfer between a first user device and a second userdevice, wherein the one or more machine learning-derived interactionpatterns comprise calculated exposure levels for one or more events forcompleting the resource transfer, and wherein the resource transfercontract is for a peer-to-peer resource transfer; based on the one ormore machine learning-derived interaction patterns, generating aresource transfer contract for transferring a resource from the firstuser device to the second user device, wherein the resource transfercontract comprises a sequential flow of the one or more events;transmitting the resource transfer contract to the first user device andthe second user device; receiving revisions to the resource transfercontract from at least one of a first user associated with the firstuser device and a second user associated with the second user device;transmitting agreed-upon conditions to the first user device and thesecond user device; receiving approval of the resource transfer contractfrom the first user via the first user device and the second user viathe second user device; generating, based on receiving the approval, afinal resource transfer contract comprising the agreed-upon conditions;codifying the final resource transfer contract comprising theagreed-upon conditions into an executable file for installation on thefirst user device and the second user device, wherein the executablefile runs on the first user device and the second user device as adistributed peer-to-peer protocol; and distributing the executable fileto the first user device and the second user device.
 11. Thecomputer-implemented method of claim 10 further comprising: calculatingan exposure level for each of the one or more events of the resourcetransfer contract based on the one or more machine learning-derivedinteraction patterns; identifying that the exposure level for at leastone of the one or more events exceeds a predetermined threshold; andbased on the exposure level exceeding the predetermined threshold,modifying the sequential flow of the one or more events, whereinmodifying the sequential flow of the one or more events comprisesinserting an additional security event to the sequential flow.
 12. Thecomputer-implemented method of claim 10 further comprising: receiving amodification to the sequential flow of the one or more events from atleast one of the first user device and the second user device;generating a modified resource transfer contract based on themodification; and calculating a modified exposure level for the modifiedresource transfer contract, wherein the one or more events and themodification are input into the machine learning engine.
 13. Thecomputer-implemented method of claim 12 further comprising iterativelymodifying the sequential flow of the one or more events until themodified exposure level is below a predetermined threshold limit. 14.The computer-implemented method of claim 12 further comprisingterminating the resource transfer contract when the modified exposurelevel exceeds a predetermined threshold limit.
 15. Thecomputer-implemented method of claim 10, wherein the historical andstreaming interaction data comprises one or more of resource data, aresource transfer type, user data of a first user associated with thefirst user device and a second user associated with the second userdevice, historical resource transfer data, and historicalmisappropriation data.